Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults.

Cardiorespiratory fitness was found to influence age-related changes of resting state brain network organization. However, the influence on dedifferentiated involvement of wider and more unspecialized brain regions during task completion is barely understood. We analyzed EEG data recorded during rest and different tasks (sensory, motor, cognitive) with dynamic mode decomposition, which accounts for topological characteristics as well as temporal dynamics of brain networks. As a main feature the dominant spatio-temporal EEG pattern was extracted in multiple frequency bands per participant. To deduce a pattern's stability, we calculated its proportion of total variance among all activation patterns over time for each task. By comparing fit (N = 15) and less fit older adults (N = 16) characterized by their performance on a 6-min walking test, we found signs of a lower task specificity of the obtained network features for the less fit compared to the fit group. This was indicated by fewer significant differences between tasks in the theta and high beta frequency band in the less fit group. Repeated measures ANOVA revealed that a significantly lower proportion of total variance can be explained by the main pattern in high beta frequency range for the less fit compared to the fit group [F(1,29) = 12.572, p = .001, partial η2 = .300]. Our results indicate that the dedifferentiation in task-related brain activation is lower in fit compared to less fit older adults. Thus, our study supports the idea that cardiorespiratory fitness influences task-related brain network organization in different task domains. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s11571-020-09656-9) contains supplementary material, which is available to authorized users.

Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics.

It is well-established that expertise developed through continuous and deliberate practice has the potential to delay age-related decline in fine motor skills. However, less is known about the underlying mechanisms, that is, whether expertise leads to a higher performance level changing the initial status from which age-related decline starts or if expertise-related changes result in qualitatively different motor output and neural processing providing a resource of compensation for age-related changes. Thus, as a first step, this study aims at a better understanding of expertise-related changes in fine motor control with respect to force output and respective electrophysiological correlates. Here, using a multidimensional approach, we investigated fine motor control of experts and novices in precision mechanics during the execution of a dynamic force control task. On the level of force output, we analyzed precision, variability, and complexity. We further used dynamic mode decomposition (DMD) to analyze the electrophysiological correlates of force control to deduce brain network dynamics. Experts' force output was more precise, less variable, and more complex. Task-related DMD mean mode magnitudes within the α-band at electrodes over sensorimotor relevant areas were reduced in experts, and lower DMD mean mode magnitudes related to the force output in novices. Our results provide evidence for expertise dependent central adaptions with distinct and more complex organization and decentralization of sensorimotor subsystems. Results from our multidimensional approach can be seen as a step forward in understanding expertise-related changes and exploiting their potential as resources for healthy aging.

Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control.

Age-related deterioration of force control is evident on behavioral and neural levels. Extensive and deliberate practice can decrease these changes. This study focused on detecting electrophysiological correlates of age- and expertise-related differences in force control. We examined young (20-27 years) and late middle-aged (57-67 years) novices as well as late middle-aged experts in the field of fine motor control. Therefore, EEG data were recorded while participants performed a force maintenance task. Variability and complexity of force data were analyzed. To detect electrophysiological correlates, dynamic mode decomposition (DMD) was applied to EEG data. DMD allows assessing brain network dynamics by extracting electrode interrelations and their dynamics. Defining clusters of electrodes, we focused on sensorimotor and attentional networks. We confirmed that force control in late middle-aged novices was more variable and less complex than in other groups. Analysis of task-related overall network characteristics, showed a decrease within the α band and increase within low ß, high ß, and  θ  band. Compared to the other groups young novices presented a decreased α magnitude. High ß magnitude was lower in late middle-aged novices than for other groups. Comparing left and right hands' performance, young novices showed higher low ß magnitude for the left hand. Late middle-aged novices showed high values for both hands while late middle-aged experts showed higher values for the right than for their left hand. Activation of attentional networks was lower in late middle-aged experts compared to novices. These results may relate to different control strategies of the three groups.

Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics.

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

Transport of Mars-crossing asteroids from the quasi-Hilda region.

We employ set oriented methods in combination with graph partitioning algorithms to identify key dynamical regions in the Sun-Jupiter-particle three-body system. Transport rates from a region near the 3:2 Hilda resonance into the realm of orbits crossing Mars' orbit are computed. In contrast to common numerical approaches, our technique does not depend on single long term simulations of the underlying model. Thus, our statistical results are particularly reliable since they are not affected by a dynamical behavior which is almost nonergodic (i.e., dominated by strongly almost invariant sets).

Return time dynamics as a tool for finding almost invariant sets.

The primary goal of transport theory is to compute the rate at which parts of the phase space of a given dynamical system move from one region to another. In this paper we present a new approach for the identification of those regions in phase space that are relevant for transport computations. More concretely, we construct a decomposition into almost invariant sets-that is, those sets that represent the main sources and sinks for transport phenomena-using return time dynamics. We illustrate this technique by partitioning a certain Poincaré section in the planar circular restricted three body problem into various sets.

Exploring invariant sets and invariant measures.

We propose a method to explore invariant measures of dynamical systems. The method is based on numerical tools which directly compute invariant sets using a subdivision technique, and invariant measures by a discretization of the Frobenius-Perron operator. Appropriate visualization tools help to analyze the numerical results and to understand important aspects of the underlying dynamics. This will be illustrated for examples provided by the Lorenz system. (c) 1997 American Institute of Physics.